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Summary: Renewable energy such as wind power plays an important role in power generation. Large amount of renewable energy can allow these producers to change the market price and be as a price maker in the market. Thus, we propose an optimal bidding strategy in the day‐ahead market for the hybrid power plant that participates as a price maker in the day‐ahead market and as a price taker in the balancing market. Hybrid power plant is composed of wind power producer, battery storage and demand response. The uncertainty of day‐ahead market price is modeled through Robust Optimization. In case studies, bidding strategy of hybrid power plant as a price maker and price taker configuration is presented. Then, joint operation of hybrid power plant is compared to disjoint operation and the effect of participating in market as coordinated unit on total profit is investigated. Moreover, the changes in profit according to risk level are determined and finally, offering curves are obtained by dividing the confidence bounds of day‐ahead market price to some subintervals. The simulation is done based on Nord Pool Market and the results validate hybrid power plant can behave as a price maker in the day‐ahead market and the profit of all units increases in joint operation. [ABSTRACT FROM AUTHOR]

Highlights: [•] The financial risk of the market price uncertainty is modelled using expected downside risk in the stochastic framework. [•] Roulette wheel mechanism and Lattice Monte Carlo Simulation are employed for random scenario generation. [•] The proposed optimization problem is solved by a decomposition technique using Benders decomposition algorithm. [ABSTRACT FROM AUTHOR]

Abstract: In view of the intermittency and uncertainty associated with both the electricity production sector of restructured power system and their competitive markets, it is necessary to develop an appropriate risk managing scheme. So that it is desirable to trade-off between optimum utilization of intermittent generation resources (i.e. renewable energy resources), uncertain market prices and related risks in order to maximize participants'' benefits and minimize the corresponding risks in the multi-product market environment. The main goal of this paper is to investigate risk management by introducing a novel multi-risk index to quantify expected downside risk (EDR) which is caused by both the wind power and market price uncertainties. Value-at-Risk (VaR) method is used to assess the mentioned risk issue by the proposed weighted EDR, so that an optimal trade-off between the profit and risk is made for the system operations. Also, the roulette wheel mechanism is employed for random market price scenario generation wherein the stochastic procedure is converted into its respective deterministic equivalents. Moreover, the autoregressive integrated moving average (ARIMA) model is employed to characterize the stochastic wind farm (WF) generation by predetermined mean level and standard deviation of wind behavior as well as temporal correlation. The problem is formulated as a mixed-integer stochastic framework for a hydro-wind power system scheduling and tested on a generation company (GENCO). [Copyright &y& Elsevier]